Robust image authentication via locality sensitive hashing with core alignment

Robust image hashing is a promising technique to represent image's perceptual content. However, when it comes to image authentication, tradeoff between robustness and discrimination is a non-negligible issue. The allowed content preserving operations and sensitive malicious manipulations on images are quite subjective to human's perception. So it needs tactics to design good hashing methods. In this paper we incorporate the novel concept of core alignment into hashing, where the proposed core alignment improves the performances of balance. First, we formulize the hashing as a supervised minimal optimization problem based on Locality Sensitive Hashing, in which p-stable distribution is exploited to maintain high dimensional locality features. Then we solve this problem by two sub-optimization problems, i.e., searching for optimal shift and searching for optimal quantization intervals. By using particle swarm optimization and simulated annealing programming approaches we develop two stochastic solutions to those two problems, respectively. Experimental results show that our proposed hashing optimizations can find optimal solutions with limited steps, and the hashing method is superior to other state-of-the-art methods in terms of authentication and robustness.